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Scalable Computational Frameworks for Next-Generation Sequencing Analysis and Gene Set Integration
Scalable Computational Frameworks for Next-Generation Sequencing Analysis and Gene Set Integration
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151515
- ISBN
- 9798383607848
- DDC
- 574
- 서명/저자
- Scalable Computational Frameworks for Next-Generation Sequencing Analysis and Gene Set Integration
- 발행사항
- [Sl] : University of California, Davis, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 85 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-02, Section: B.
- 주기사항
- Advisor: Brown, C. Titus.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Davis, 2024.
- 초록/해제
- 요약The rapid growth in biomedical research has generated vast amounts of data, including genomic, molecular, imaging, and clinical information from humans and other species. Leveraging this data is essential for groundbreaking scientific discoveries and a deeper understanding of health and disease across different species. However, the complexity and volume of these datasets present significant computational challenges, limiting their potential.This dissertation addresses two key challenges in biomedical data analysis: the efficient evaluation of sequencing data and the effective management and analysis of gene sets. By focusing on these areas, we develop innovative computational methods that enable the rapid, scalable, and accurate processing of large-scale biomedical data. For sequencing data, we create algorithms that enhance the speed and precision of data evaluation, making it feasible to manage the increasing volume of sequences generated by modern technologies. For gene sets, we devise tools for their efficient management and analysis, allowing researchers to draw meaningful insights from complex genetic information.Through this research, we aim to contribute to the development of new analytical tools and methods, ultimately supporting the advancement of precision medicine and personalized healthcare for both human and veterinary applications.
- 일반주제명
- Bioinformatics
- 일반주제명
- Computer science
- 일반주제명
- Biostatistics
- 일반주제명
- Molecular biology
- 키워드
- Alignment-free
- 키워드
- Coverage
- 키워드
- Gene sets
- 키워드
- K-mer-based
- 키워드
- Sequencing data
- 기타저자
- University of California, Davis Computer Science
- 기본자료저록
- Dissertations Abstracts International. 86-02B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
008250123s2024 us c eng d■001000017162022
■00520250211151515
■006m o d
■007cr#unu||||||||
■020 ▼a9798383607848
■035 ▼a(MiAaPQ)AAI31300481
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a574
■1001 ▼aAbuelanin, Mohamed.
■24510▼aScalable Computational Frameworks for Next-Generation Sequencing Analysis and Gene Set Integration
■260 ▼a[Sl]▼bUniversity of California, Davis▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a85 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-02, Section: B.
■500 ▼aAdvisor: Brown, C. Titus.
■5021 ▼aThesis (Ph.D.)--University of California, Davis, 2024.
■520 ▼aThe rapid growth in biomedical research has generated vast amounts of data, including genomic, molecular, imaging, and clinical information from humans and other species. Leveraging this data is essential for groundbreaking scientific discoveries and a deeper understanding of health and disease across different species. However, the complexity and volume of these datasets present significant computational challenges, limiting their potential.This dissertation addresses two key challenges in biomedical data analysis: the efficient evaluation of sequencing data and the effective management and analysis of gene sets. By focusing on these areas, we develop innovative computational methods that enable the rapid, scalable, and accurate processing of large-scale biomedical data. For sequencing data, we create algorithms that enhance the speed and precision of data evaluation, making it feasible to manage the increasing volume of sequences generated by modern technologies. For gene sets, we devise tools for their efficient management and analysis, allowing researchers to draw meaningful insights from complex genetic information.Through this research, we aim to contribute to the development of new analytical tools and methods, ultimately supporting the advancement of precision medicine and personalized healthcare for both human and veterinary applications.
■590 ▼aSchool code: 0029.
■650 4▼aBioinformatics
■650 4▼aComputer science
■650 4▼aBiostatistics
■650 4▼aMolecular biology
■653 ▼aAlignment-free
■653 ▼aCoverage
■653 ▼aGene sets
■653 ▼aK-mer-based
■653 ▼aSequencing data
■690 ▼a0715
■690 ▼a0984
■690 ▼a0307
■690 ▼a0308
■71020▼aUniversity of California, Davis▼bComputer Science.
■7730 ▼tDissertations Abstracts International▼g86-02B.
■790 ▼a0029
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17162022▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
